Can Solar Open 2 250B run on GTX 1650 4GB?

Q: What should I upgrade first if Solar Open 2 250B feels slow on GTX 1650 4GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

NO — Won't Fit

F0Won't run

Estimated from fit model

Solar Open 2 250B needs ~156.9 GB but GTX 1650 4GB only has 4.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: Very lowStack: StandardBottleneck: Memory capacity

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 154.7 GB, exceeds 4.0 GB available

154.7 GB required4.0 GB available

3867% VRAM needed

150.7 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.0 tok/s

TTFT

96800 ms

Safe context

Memory

154.7 GB / 4.0 GB

Offload

100%

Memory breakdown

Weights152.7 GB

KV Cache0.7 GB

Runtime0.9 GB

Headroom0.4 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsSolar Open 2 250B on GTX 1650 4GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort:

def quicksort(arr):
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

# Example usage
numbers = [3, 6, 8, 10, 1, 2, 1]
print(quicksort(numbers))  # [1, 1, 2, 3, 6, 8, 10]

The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]:
    """Sort a list using the quicksort algorithm.

    Args:
        arr: List of integers to sort.

    Returns:
        A new sorted list.
    """
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 2.0 tok/s decode · 96.8s TTFT (warm) · 5 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 156.9 GB, but this setup only exposes 4.0 GB of usable VRAM.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	2.0 tok/s	52800 ms	4K
Coding	F	Too heavy	2.0 tok/s	96800 ms	4K
Agentic Coding	F	Too heavy	2.0 tok/s	140800 ms	4K
Reasoning	F	Too heavy	2.0 tok/s	114400 ms	4K
RAG	F	Too heavy	2.0 tok/s	176000 ms	4K

Inference speed

Solar Open 2 250B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Solar Open 2 250B at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is Mac Studio M3 Ultra 256GB at ~18 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	17.7	Offloads

Quantization options

How Solar Open 2 250B (250.3000030517578B params) fits at each quantization level on GTX 1650 4GB (4.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q1_0_G128	1.125	36.0 GB	Very Low	F0
Q2_0_G128	1.71	66.8 GB	Low	F0
Q2_K	2

Upgrade options

Hardware that runs Solar Open 2 250B well

NVIDIA B200 180GBBudget pick

180 GB VRAM (+176)8000 GB/s (+7872)

Makes the model fit on the accelerator instead of staying completely out of reach.137.6 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$30,000 MSRP

NVIDIA H200 141GBNVIDIA upgrade

141 GB VRAM (+137)4800 GB/s (+4672)

Makes the model fit on the accelerator instead of staying completely out of reach.52.5 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 2525%.

~$30,000 MSRP

B100 192GBBest value

192 GB VRAM (+188)8000 GB/s (+7872)

Makes the model fit on the accelerator instead of staying completely out of reach.137.6 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$35,000 MSRP

Frequently asked questions

Can GTX 1650 4GB run Solar Open 2 250B?

No, Solar Open 2 250B requires more memory than GTX 1650 4GB provides.

How much VRAM does Solar Open 2 250B need?

Solar Open 2 250B (250.3000030517578B parameters) requires approximately 156.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Solar Open 2 250B?

The recommended quantization for Solar Open 2 250B is Q4_K_M, which balances quality and memory efficiency.

What speed will Solar Open 2 250B run at on GTX 1650 4GB?

On GTX 1650 4GB, Solar Open 2 250B achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using Q4_K_M quantization.

Can GTX 1650 4GB run Solar Open 2 250B for coding?

For coding workloads, Solar Open 2 250B on GTX 1650 4GB receives a F grade with 2.0 tok/s and 4K context.

What context window can Solar Open 2 250B use on GTX 1650 4GB?

On GTX 1650 4GB, Solar Open 2 250B can safely use up to 4K tokens of context. The model's official context limit is 1.0M, but available memory constrains the safe maximum.

What should I upgrade first if Solar Open 2 250B feels slow on GTX 1650 4GB?

See all results for GTX 1650 4GB See all hardware for Solar Open 2 250B

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